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         <a href="http://pan.baidu.com/s/1EaLnZ">
          http://pan.baidu.com/s/1EaLnZ
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        </li>
        <li>
         机器学习经典书籍小结
         <a href="http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html">
          http://www.cnblogs.com/snake-hand/archive/2013/06/10/3131145.html
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        <li>
         机器学习&amp;深度学习经典资料汇总
         <a href="http://www.thebigdata.cn/JiShuBoKe/13299.html">
          http://www.thebigdata.cn/JiShuBoKe/13299.html
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        视频
       </h2>
       <ul>
        <li>
         浙大数据挖掘系列
         <a href="http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765">
          http://v.youku.com/v_show/id_XNTgzNDYzMjg=.html?f=2740765
         </a>
        </li>
        <li>
         用Python做科学计算
         <a href="http://www.tudou.com/listplay/fLDkg5e1pYM.html">
          http://www.tudou.com/listplay/fLDkg5e1pYM.html
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         R语言视频
         <a href="http://pan.baidu.com/s/1koSpZ">
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         Hadoop视频
         <a href="http://pan.baidu.com/s/1b1xYd">
          http://pan.baidu.com/s/1b1xYd
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        <li>
         42区 . 技术 .
         <a href="http://www.kuqin.com/chuangye/" target="_blank">
          创业
         </a>
         . 第二讲
         <a href="http://v.youku.com/v_show/id_XMzAyMDYxODUy.html">
          http://v.youku.com/v_show/id_XMzAyMDYxODUy.html
         </a>
        </li>
        <li>
         加州理工学院公开课：机器学习与数据挖掘
         <a href="http://v.163.com/special/opencourse/learningfromdata.html">
          http://v.163.com/special/opencourse/learningfromdata.html
         </a>
        </li>
       </ul>
       <h2>
        QQ群
       </h2>
       <ul>
        <li>
         机器学习&amp;模式识别 246159753
        </li>
        <li>
         数据挖掘机器学习 236347059
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         推荐系统 274750470
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        <li>
         机器学习群：463848712
        </li>
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       <h2>
        Github
       </h2>
       <h3>
        推荐系统
       </h3>
       <ul>
        <li>
         推荐系统开源软件列表汇总和评点
         <a href="http://in.sdo.com/?p=1707">
          http://in.sdo.com/?p=1707
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         Mrec(Python)
         <a href="https://github.com/mendeley/mrec">
          https://github.com/mendeley/mrec
         </a>
        </li>
        <li>
         Crab(Python)
         <a href="https://github.com/muricoca/crab">
          https://github.com/muricoca/crab
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        <li>
         Python-recsys(Python)
         <a href="https://github.com/ocelma/python-recsys">
          https://github.com/ocelma/python-recsys
         </a>
        </li>
        <li>
         CofiRank(C++)
         <a href="https://github.com/markusweimer/cofirank">
          https://github.com/markusweimer/cofirank
         </a>
        </li>
        <li>
         GraphLab(C++)
         <a href="https://github.com/graphlab-code/graphlab">
          https://github.com/graphlab-code/graphlab
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        <li>
         EasyRec(Java)
         <a href="https://github.com/hernad/easyrec">
          https://github.com/hernad/easyrec
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          https://github.com/apache/mahout
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        <li>
         Recommendable(Ruby)
         <a href="https://github.com/davidcelis/recommendable">
          https://github.com/davidcelis/recommendable
         </a>
        </li>
       </ul>
       <h3>
        库
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        <li>
         NLTK
         <a href="https://github.com/nltk/nltk">
          https://github.com/nltk/nltk
         </a>
        </li>
        <li>
         Pattern
         <a href="https://github.com/clips/pattern">
          https://github.com/clips/pattern
         </a>
        </li>
        <li>
         Pyrallel
         <a href="https://github.com/pydata/pyrallel">
          https://github.com/pydata/pyrallel
         </a>
        </li>
        <li>
         Theano
         <a href="https://github.com/Theano/Theano">
          https://github.com/Theano/Theano
         </a>
        </li>
        <li>
         Pylearn2
         <a href="https://github.com/lisa-lab/pylearn2">
          https://github.com/lisa-lab/pylearn2
         </a>
        </li>
        <li>
         TextBlob
         <a href="https://github.com/sloria/TextBlob">
          https://github.com/sloria/TextBlob
         </a>
        </li>
        <li>
         MBSP
         <a href="https://github.com/clips/MBSP">
          https://github.com/clips/MBSP
         </a>
        </li>
        <li>
         Gensim
         <a href="https://github.com/piskvorky/gensim">
          https://github.com/piskvorky/gensim
         </a>
        </li>
        <li>
         Langid.py
         <a href="https://github.com/saffsd/langid.py">
          https://github.com/saffsd/langid.py
         </a>
        </li>
        <li>
         Jieba
         <a href="https://github.com/fxsjy/jieba">
          https://github.com/fxsjy/jieba
         </a>
        </li>
        <li>
         xTAS
         <a href="https://github.com/NLeSC/xtas">
          https://github.com/NLeSC/xtas
         </a>
        </li>
        <li>
         NumPy
         <a href="https://github.com/numpy/numpy">
          https://github.com/numpy/numpy
         </a>
        </li>
        <li>
         SciPy
         <a href="https://github.com/scipy/scipy">
          https://github.com/scipy/scipy
         </a>
        </li>
        <li>
         Matplotlib
         <a href="https://github.com/matplotlib/matplotlib">
          https://github.com/matplotlib/matplotlib
         </a>
        </li>
        <li>
         scikit-learn
         <a href="https://github.com/scikit-learn/scikit-learn">
          https://github.com/scikit-learn/scikit-learn
         </a>
        </li>
        <li>
         Pandas
         <a href="https://github.com/pydata/pandas">
          https://github.com/pydata/pandas
         </a>
        </li>
        <li>
         MDP
         <a href="http://mdp-toolkit.sourceforge.net/">
          http://mdp-toolkit.sourceforge.net/
         </a>
        </li>
        <li>
         PyBrain
         <a href="https://github.com/pybrain/pybrain">
          https://github.com/pybrain/pybrain
         </a>
        </li>
        <li>
         PyML
         <a href="http://pyml.sourceforge.net/">
          http://pyml.sourceforge.net/
         </a>
        </li>
        <li>
         Milk
         <a href="https://github.com/luispedro/milk">
          https://github.com/luispedro/milk
         </a>
        </li>
        <li>
         PyMVPA
         <a href="https://github.com/PyMVPA/PyMVPA">
          https://github.com/PyMVPA/PyMVPA
         </a>
        </li>
       </ul>
       <h2>
        博客
       </h2>
       <ul>
        <li>
         周涛
         <a href="http://blog.sciencenet.cn/home.php?mod=space&amp;uid=3075">
          http://blog.sciencenet.cn/home.php?mod=space&amp;uid=3075
         </a>
        </li>
        <li>
         Greg Linden
         <a href="http://glinden.blogspot.com/">
          http://glinden.blogspot.com/
         </a>
        </li>
        <li>
         Marcel Caraciolo
         <a href="http://aimotion.blogspot.com/">
          http://aimotion.blogspot.com/
         </a>
        </li>
        <li>
         RsysChina
         <a href="http://weibo.com/p/1005051686952981">
          http://weibo.com/p/1005051686952981
         </a>
        </li>
        <li>
         推荐系统人人小站
         <a href="http://zhan.renren.com/recommendersystem">
          http://zhan.renren.com/recommendersystem
         </a>
        </li>
        <li>
         阿稳
         <a href="http://www.wentrue.net/">
          http://www.wentrue.net
         </a>
        </li>
        <li>
         梁斌
         <a href="http://weibo.com/pennyliang">
          http://weibo.com/pennyliang
         </a>
        </li>
        <li>
         刁瑞
         <a href="http://diaorui.net/">
          http://diaorui.net
         </a>
        </li>
        <li>
         guwendong
         <a href="http://www.guwendong.com/">
          http://www.guwendong.com
         </a>
        </li>
        <li>
         xlvector
         <a href="http://xlvector.net/">
          http://xlvector.net
         </a>
        </li>
        <li>
         懒惰啊我
         <a href="http://www.cnblogs.com/flclain/">
          http://www.cnblogs.com/flclain/
         </a>
        </li>
        <li>
         free mind
         <a href="http://blog.pluskid.org/">
          http://blog.pluskid.org/
         </a>
        </li>
        <li>
         lovebingkuai
         <a href="http://lovebingkuai.diandian.com/">
          http://lovebingkuai.diandian.com/
         </a>
        </li>
        <li>
         LeftNotEasy
         <a href="http://www.cnblogs.com/LeftNotEasy">
          http://www.cnblogs.com/LeftNotEasy
         </a>
        </li>
        <li>
         LSRS 2013
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          http://www.kuqin.com/shuoit/20151130/349205.html
         </a>
        </li>
        <li>
         Google小组
         <a href="https://groups.google.com/forum/#%21forum/resys">
          https://groups.google.com/forum/#!forum/resys
         </a>
        </li>
        <li>
         Journal of Machine Learning Research
         <a href="http://jmlr.org/">
          http://jmlr.org/
         </a>
        </li>
        <li>
         在线的机器学习社区
         <a href="http://www.52ml.net/16336.html">
          http://www.52ml.net/16336.html
         </a>
        </li>
        <li>
         清华大学信息检索组
         <a href="http://166.111.138.23:8980/cms/">
          http://www.thuir.cn
         </a>
        </li>
        <li>
         我爱自然语言处理
         <a href="http://www.52nlp.cn/">
          http://www.52nlp.cn/
         </a>
        </li>
        <li>
         数盟社区：
         <a href="http://dataunion.org/" target="_blank">
          http://dataunion.org/
         </a>
        </li>
       </ul>
       <h2>
        文章
       </h2>
       <ul>
        <li>
         心中永远的正能量
         <a href="http://blog.csdn.net/yunlong34574">
          http://blog.csdn.net/yunlong34574
         </a>
        </li>
        <li>
         机器学习最佳入门学习资料汇总
         <a href="http://www.kuqin.com/shuoit/20140625/340777.html">
          《机器学习最佳入门学习资料汇总》
         </a>
        </li>
        <li>
         Books for Machine Learning with R
         <a href="http://www.52ml.net/16312.html">
          http://www.52ml.net/16312.html
         </a>
        </li>
        <li>
         是什么阻碍了你的机器学习目标？
         <a href="http://www.52ml.net/16436.htm">
          http://www.52ml.net/16436.htm
         </a>
        </li>
        <li>
         推荐系统初探
         <a href="http://yongfeng.me/attach/rs-survey-zhang-slices.pdf">
          http://yongfeng.me/attach/rs-survey-zhang-slices.pdf
         </a>
        </li>
        <li>
         推荐系统中协同过滤算法若干问题的研究
         <a href="http://pan.baidu.com/s/1bnjDBYZ">
          http://pan.baidu.com/s/1bnjDBYZ
         </a>
        </li>
        <li>
         Netflix 推荐系统：第一部分
         <a href="http://blog.csdn.net/bornhe/article/details/8222450">
          http://blog.csdn.net/bornhe/article/details/8222450
         </a>
        </li>
        <li>
         Netflix 推荐系统：第二部分
         <a href="http://blog.csdn.net/bornhe/article/details/8222497">
          http://blog.csdn.net/bornhe/article/details/8222497
         </a>
        </li>
        <li>
         探索推荐引擎内部的秘密
         <a href="http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html">
          http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html
         </a>
        </li>
        <li>
         推荐系统resys小组线下活动见闻2009-08-22
         <a href="http://www.tuicool.com/articles/vUvQVn">
          http://www.tuicool.com/articles/vUvQVn
         </a>
        </li>
        <li>
         Recommendation Engines Seminar Paper, Thomas Hess, 2009: 推荐引擎的总结性文章
         <a href="http://www.slideshare.net/antiraum/recommender-engines-seminar-paper">
          http://www.slideshare.net/antiraum/recommender-engines-seminar-paper
         </a>
        </li>
        <li>
         Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Adomavicius, G.; Tuzhilin, A., 2005
         <a href="http://dl.acm.org/citation.cfm?id=1070751">
          http://dl.acm.org/citation.cfm?id=1070751
         </a>
        </li>
        <li>
         A Taxonomy of RecommenderAgents on the Internet, Montaner, M.; Lopez, B.; de la Rosa, J. L., 2003
         <a href="http://link.springer.com/10.1023%2FA%3A1022850703159?LI=true&amp;from=SL">
          http://www.springerlink.com/index/KK844421T5466K35.pdf
         </a>
        </li>
        <li>
         A Course in Machine Learning
         <a href="http://ciml.info/">
          http://ciml.info/
         </a>
        </li>
        <li>
         基于mahout构建社会化推荐引擎
         <a href="http://www.doc88.com/p-745821989892.html">
          http://www.doc88.com/p-745821989892.html
         </a>
        </li>
        <li>
         个性化推荐技术漫谈
         <a href="http://blog.csdn.net/java060515/article/details/1570243">
          http://blog.csdn.net/java060515/archive/2007/04/19/1570243.aspx
         </a>
        </li>
        <li>
         Design of Recommender System
         <a href="http://www.slideshare.net/rashmi/design-of-recommender-systems">
          http://www.slideshare.net/rashmi/design-of-recommender-systems
         </a>
        </li>
        <li>
         How to build a recommender system
         <a href="http://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation">
          http://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation
         </a>
        </li>
        <li>
         推荐系统架构小结
         <a href="http://blog.csdn.net/idonot/article/details/7996733">
          http://blog.csdn.net/idonot/article/details/7996733
         </a>
        </li>
        <li>
         System Architectures for Personalization and Recommendation
         <a href="http://techblog.netflix.com/2013/03/system-architectures-for.html">
          http://techblog.netflix.com/2013/03/system-architectures-for.html
         </a>
        </li>
        <li>
         The Netflix Tech Blog
         <a href="http://techblog.netflix.com/">
          http://techblog.netflix.com/
         </a>
        </li>
        <li>
         百分点推荐引擎——从需求到架构
         <a href="http://www.kuqin.com/system-analysis/20111130/315590.html">
          《百分点推荐引擎——从需求到架构》
         </a>
        </li>
        <li>
         推荐系统 在InfoQ上的内容
         <a href="http://www.infoq.com/cn/recommend">
          http://www.infoq.com/cn/recommend
         </a>
        </li>
        <li>
         推荐系统实时化的实践和思考
         <a href="http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking">
          http://www.infoq.com/cn/presentations/recommended-system-real-time-practice-thinking
         </a>
        </li>
        <li>
         质量保证的推荐实践
         <a href="http://www.infoq.com/cn/news/2013/10/testing-practice/">
          http://www.infoq.com/cn/news/2013/10/testing-practice/
         </a>
        </li>
        <li>
         推荐系统的工程挑战
         <a href="http://www.infoq.com/cn/presentations/Recommend-system-engineering">
          http://www.infoq.com/cn/presentations/Recommend-system-engineering
         </a>
        </li>
        <li>
         社会化推荐在人人网的应用
         <a href="http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/">
          http://www.infoq.com/cn/articles/zyy-social-recommendation-in-renren/
         </a>
        </li>
        <li>
         利用20%时间开发推荐引擎
         <a href="http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine">
          http://www.infoq.com/cn/presentations/twenty-percent-time-to-develop-recommendation-engine
         </a>
        </li>
        <li>
         使用Hadoop和 Mahout实现推荐引擎
         <a href="http://www.alexa.com/siteinfo/jdon.com">
          http://www.jdon.com/44747
         </a>
        </li>
        <li>
         SVD 简介
         <a href="http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html">
          http://www.cnblogs.com/FengYan/archive/2012/05/06/2480664.html
         </a>
        </li>
        <li>
         Netflix推荐系统：从评分预测到消费者法则
         <a href="http://blog.csdn.net/lzt1983/article/details/7696578">
          http://blog.csdn.net/lzt1983/article/details/7696578
         </a>
        </li>
       </ul>
       <h2>
        论文
       </h2>
       <ul>
        <li>
         <a href="http://public.research.att.com/%7Evolinsky/netflix/kdd08koren.pdf">
          (Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model) P195
         </a>
        </li>
        <li>
         <a href="http://www.mimuw.edu.pl/%7Epaterek/ap_kdd.pdf">
          (Improving regularized singular value decomposition for collaborative filtering) P195
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Least Squares Wikipedia) P195
         </a>
        </li>
        <li>
         <a href="http://sydney.edu.au/engineering/it/%7Ejosiah/lemma/kdd-fp074-koren.pdf">
          (Collaborative filtering with temporal dynamics) P193
         </a>
        </li>
        <li>
         <a href="http://nlpr-web.ia.ac.cn/2009papers/gjhy/gh26.pdf">
          (Time-dependent Models in Collaborative Filtering based Recommender System) P193
         </a>
        </li>
        <li>
         <a href="http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf">
          (Factor in the Neighbors: Scalable and Accurate Collaborative Filtering) P190
         </a>
        </li>
        <li>
         <a href="http://sifter.org/%7Esimon/journal/20061211.html">
          (Simon Funk Blog:Funk SVD) P187
         </a>
        </li>
        <li>
         <a href="http://web.stanford.edu/%7Ekoutrika/Readings/res/Default/billsus98learning.pdf">
          (Learning Collaborative Information Filters) P186
         </a>
        </li>
        <li>
         <a href="http://files.grouplens.org/papers/www10_sarwar.pdf">
          (item-based collaborative filtering recommendation algorithms) P185
         </a>
        </li>
        <li>
         <a href="http://mymediaproject.codeplex.com/">
          (MyMedia Project) P178
         </a>
        </li>
        <li>
         <a href="http://tech.hulu.com/blog/2011/09/19/recommendation-system/">
          (Hulu 推荐系统架构) P178
         </a>
        </li>
        <li>
         <a href="http://www.cs.cmu.edu/%7Edeepay/mywww/papers/www08-interaction.pdf">
          (Contextual Advertising by Combining Relevance with Click Feedback) P177
         </a>
        </li>
        <li>
         <a href="http://www.google.com.hk/url?sa=t&amp;rct=j&amp;q=online+learning+from+click+data+spnsored+search&amp;amp;source=web&amp;cd=1&amp;ved=0CFkQFjAA&amp;amp;url=http%3A%2F%2Fwww.research.yahoo.net%2Ffiles%2Fp227-ciaramita.pdf&amp;ei=HY8JUJW8CrGuiQfpx-XyCQ&amp;usg=AFQjCNE_CYbEs8DVo84V-0VXs5FeqaJ5GQ&amp;cad=rjt">
          (Online Learning from Click Data for Sponsored Search) P177
         </a>
        </li>
        <li>
         <a href="http://olivier.chapelle.cc/pub/DBN_www2009.pdf">
          (A Dynamic Bayesian Network Click Model for Web Search Ranking) P177
         </a>
        </li>
        <li>
         <a href="http://www.google.com.hk/url?sa=t&amp;rct=j&amp;q=social+recommendation+using+prob&amp;source=web&amp;amp;cd=2&amp;ved=0CFcQFjAB&amp;url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.141.465%26rep%3Drep1%26type%3Dpdf&amp;amp;ei=LY0JUJ7OL9GPiAfe8ZzyCQ&amp;usg=AFQjCNH-xTUWrs9hkxTA8si5fztAdDAEng">
          (SoRec: Social Recommendation Using Probabilistic Matrix) P165
         </a>
        </li>
        <li>
         <a href="http://www.research.ibm.com/index.shtml">
          (“Make New Friends, but Keep the Old” – Recommending People on Social Networking Sites) P164
         </a>
        </li>
        <li>
         <a href="http://blog.linkedin.com/2008/04/11/learn-more-abou-2/">
          (Learn more about “People You May Know”) P160
         </a>
        </li>
        <li>
         <a href="http://files.grouplens.org/papers/p217-chen.pdf">
          (Speak Little and Well: Recommending Conversations in Online Social Streams) P158
         </a>
        </li>
        <li>
         <a href="http://techcrunch.com/2010/04/22/facebook-edgerank/">
          (EdgeRank: The Secret Sauce That Makes Facebook’s News Feed Tick) P157
         </a>
        </li>
        <li>
         <a href="http://www.ercim.eu/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf">
          (Comparing Recommendations Made by Online Systems and Friends) P155
         </a>
        </li>
        <li>
         <a href="http://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;amp;source=web&amp;cd=2&amp;ved=0CGQQFjAB&amp;url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.165.3679%26rep%3Drep1%26type%3Dpdf&amp;ei=dIIJUMzEE8WviQf5tNjcCQ&amp;usg=AFQjCNGw2bHXJ6MdYpksL66bhUE8krS41w&amp;sig2=5EcEDhRe9S5SQNNojWk7_Q">
          (Recommendations in taste related domains) P155
         </a>
        </li>
        <li>
         <a href="http://www.infoq.com/news/2009/06/Twitter-Architecture/">
          (Twitter, an Evolving Architecture) P154
         </a>
        </li>
        <li>
         <a href="http://www.comp.hkbu.edu.hk/%7Elichen/download/p245-yuan.pdf">
          (Factorization vs. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation) P153
         </a>
        </li>
        <li>
         <a href="http://www.dai-labor.de/camra2010/">
          (Workshop on Context-awareness in Retrieval and Recommendation) P151
         </a>
        </li>
        <li>
         <a href="http://snap.stanford.edu/data/">
          (Stanford Large Network Dataset Collection) P149
         </a>
        </li>
        <li>
         <a href="http://www.nielsen.com/us/en/insights/news/2011/friends-frenemies-why-we-add-and-remove-facebook-friends.html">
          (Friends &amp; Frenemies: Why We Add and Remove Facebook Friends) P147
         </a>
        </li>
        <li>
         <a href="http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36371.pdf">
          (Suggesting Friends Using the Implicit Social Graph) P145
         </a>
        </li>
        <li>
         <a href="http://www.nielsen.com/us/en/insights/news/2009/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most.html">
          (Global Advertising: Consumers Trust Real Friends and Virtual Strangers the Most) P144
         </a>
        </li>
        <li>
         <a href="http://www.eng.auburn.edu/%7Eweishinn/papers/MDM2010.pdf">
          (Efﬁcient Evaluation of k-Range Nearest Neighbor Queries in Road Networks) P143
         </a>
        </li>
        <li>
         <a href="http://www.cs.umd.edu/users/meesh/420/neighbor.pdf">
          (Distance Browsing in Spatial Databases1) P142
         </a>
        </li>
        <li>
         <a href="http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html">
          (A Peek Into Netflix Queues) P141
         </a>
        </li>
        <li>
         <a href="http://xavier.amatriain.net/pubs/GeolocatedRecommendations.pdf">
          (geolocated recommendations) P140
         </a>
        </li>
        <li>
         <a href="http://readwrite.com/2010/11/15/google_launches_recommendation_engine_for_places">
          (Google Launches Hotpot, A Recommendation Engine for Places) P139
         </a>
        </li>
        <li>
         <a href="http://www.google.com/places/">
          (Hotpot) P139
         </a>
        </li>
        <li>
         <a href="http://staff.science.uva.nl/%7Ekamps/ireval/papers/paper_14.pdf">
          (Evaluating Collaborative Filtering Over Time) P129
         </a>
        </li>
        <li>
         <a href="http://www0.cs.ucl.ac.uk/staff/l.capra/publications/lathia_sigir10.pdf">
          (Temporal Diversity in Recommender Systems) P129
         </a>
        </li>
        <li>
         <a href="http://bits.blogs.nytimes.com/2011/09/07/the-lifespan-of-a-link/">
          (The Lifespan of a link) P125
         </a>
        </li>
        <li>
         <a href="http://2011.camrachallenge.com/">
          (Challenge on Context-aware Movie Recommendation) P123
         </a>
        </li>
        <li>
         <a href="http://files.grouplens.org/papers/group07-sen.pdf">
          (The Quest for Quality Tags) P120
         </a>
        </li>
        <li>
         <a href="http://files.grouplens.org/papers/tagommenders_numbered.pdf">
          (Tagommenders: Connecting Users to Items through Tags) P119
         </a>
        </li>
        <li>
         <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&amp;rep=rep1&amp;amp;type=pdf">
          (Folkrank: A ranking algorithm for folksonomies) P119
         </a>
        </li>
        <li>
         <a href="http://www.l3s.de/web/upload/documents/1/recSys09.pdf">
          (latent dirichlet allocation for tag recommendation) P119
         </a>
        </li>
        <li>
         <a href="http://delab.csd.auth.gr/papers/recsys.pdf">
          (Tag recommendations based on tensor dimensionality reduction）P119
         </a>
        </li>
        <li>
         <a href="http://www.kde.cs.uni-kassel.de/ws/rsdc08/">
          (基于标签的推荐系统比赛) P119
         </a>
        </li>
        <li>
         <a href="http://research.microsoft.com/pubs/73692/yihgoca-www06.pdf">
          (Finding Advertising Keywords on Web Pages) P118
         </a>
        </li>
        <li>
         <a href="http://www.google.com/url?sa=t&amp;rct=j&amp;q=delicious%20dataset%20dai-larbor&amp;amp;source=web&amp;cd=1&amp;ved=0CFIQFjAA&amp;url=http%3A%2F%2Fwww.dai-labor.de%2Fen%2Fcompetence_centers%2Firml%2Fdatasets%2F&amp;ei=1R4JUKyFOKu0iQfKvazzCQ&amp;;usg=AFQjCNGuVzzKIKi3K2YFybxrCNxbtKqS4A&amp;amp;cad=rjt">
          (Delicious Dataset) P101
         </a>
        </li>
        <li>
         <a href="http://web.stanford.edu/%7Emorganya/research/chi2007-tagging.pdf">
          (Why We Tag: Motivations for Annotation in Mobile and Online Media ) P100
         </a>
        </li>
        <li>
         <a href="http://www.shilad.com/shilads_thesis.pdf">
          (Nurturing Tagging Communities) P100
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Tag Wikipedia) P96
         </a>
        </li>
        <li>
         <a href="http://www.shilad.com/papers/tagsplanations_iui2009.pdf">
          (Tagsplanations: Explaining Recommendations Using Tags) P96
         </a>
        </li>
        <li>
         <a href="http://www.jinni.com/movie-genome.html">
          (Jinni Movie Genome) P94
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Pandora Music Genome Project Attributes) P94
         </a>
        </li>
        <li>
         <a href="http://www.pandora.com/about/mgp">
          (About The Music Genome Project) P94
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Kullback–Leibler divergence) P93
         </a>
        </li>
        <li>
         <a href="http://www.cs.princeton.edu/%7Eblei/papers/BleiNgJordan2003.pdf">
          (Latent Dirichlet Allocation) P92
         </a>
        </li>
        <li>
         <a href="http://tunedit.org/challenge/VLNetChallenge">
          (冷启动问题的比赛) P92
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Vector Space Model) P90
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          ( adaptive bootstrapping of recommender systems using decision trees) P87
         </a>
        </li>
        <li>
         <a href="https://www.aaai.org/ojs/index.php/aimagazine/article/view/1292">
          (LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data) P80
         </a>
        </li>
        <li>
         <a href="http://icme.stanford.edu/">
          (FAST ALGORITHMS FOR SPARSE MATRIX INVERSE COMPUTATIONS) P77
         </a>
        </li>
        <li>
         <a href="http://www-cs-students.stanford.edu/%7Etaherh/papers/topic-sensitive-pagerank.pdf">
          (Topic Sensitive Pagerank) P74
         </a>
        </li>
        <li>
         <a href="http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;arnumber=4072747&amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4072747">
          (Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation) P74
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Bipatite Graph) P73
         </a>
        </li>
        <li>
         <a href="http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf">
          (Latent Factor Models for Web Recommender Systems) P70
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Stochastic Gradient Descent) P68
         </a>
        </li>
        <li>
         <a href="http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf">
          (One-Class Collaborative Filtering) P67
         </a>
        </li>
        <li>
         <a href="http://glinden.blogspot.com/2006/03/early-amazon-similarities.html">
          (Greg Linden Blog) P63
         </a>
        </li>
        <li>
         <a href="http://www.cs.umd.edu/%7Esamir/498/Amazon-Recommendations.pdf">
          (Amazon.com Recommendations Item-to-Item Collaborative Filtering) P59
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          (Digg Vedio) P50
         </a>
        </li>
        <li>
         <a href="http://research.microsoft.com/pubs/69656/tr-98-12.pdf">
          (Empirical Analysis of Predictive Algorithms for Collaborative Filtering) P49
         </a>
        </li>
        <li>
         <a href="http://grouplens.org/datasets/movielens/">
          (MovieLens Dataset) P42
         </a>
        </li>
        <li>
         <a href="http://mmdays.com/2008/11/22/power_law_1/">
          浅谈网络世界的Power Law现象 P39
         </a>
        </li>
        <li>
         <a href="http://www.dtic.upf.edu/%7Eocelma/MusicRecommendationDataset/lastfm-1K.html">
          (Lastfm Dataset) P39
         </a>
        </li>
        <li>
         <a href="http://www2.informatik.uni-freiburg.de/%7Ecziegler/BX/">
          (Book-Crossing Dataset) P38
         </a>
        </li>
        <li>
         <a href="http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html">
          (Five Stars Dominate Ratings) P37
         </a>
        </li>
        <li>
         <a href="http://recsys.acm.org/recsys11/">
          (Tutorial on robutness of recommender system) P32
         </a>
        </li>
        <li>
         <a href="http://brettb.net/project/papers/2007%20Trust-aware%20recommender%20systems.pdf">
          (Trust-aware recommender systems) P31
         </a>
        </li>
        <li>
         <a href="http://dare.uva.nl/document/131544">
          (The effects of transparency on trust in and acceptance of a content-based art recommender) P31
         </a>
        </li>
        <li>
         <a href="http://link.springer.com/978-3-540-78196-7?from=SL">
          (Metrics for evaluating the serendipity of recommendation lists) P30
         </a>
        </li>
        <li>
         <a href="http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_21.pdf">
          (Auralist: Introducing Serendipity into Music Recommendation ) P30
         </a>
        </li>
        <li>
         <a href="http://ir.ii.uam.es/divers2011/">
          (Internation Workshop on Novelty and Diversity in Recommender Systems) p29
         </a>
        </li>
        <li>
         <a href="http://mtg.upf.edu/static/media/PhD_ocelma.pdf">
          (Music Recommendation and Discovery in the Long Tail) P29
         </a>
        </li>
        <li>
         <a href="http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf">
          (Evaluation Recommendation Systems) P27
         </a>
        </li>
        <li>
         <a href="http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext">
          (What is a Good Recomendation Algorithm?) P26
         </a>
        </li>
        <li>
         <a href="http://www.sigkdd.org/explorations/issues/9-2-2007-12/7-Netflix-2.pdf">
          (Major componets of the gravity recommender system) P25
         </a>
        </li>
        <li>
         <a href="http://www-users.cs.umn.edu/%7Emcnee/mcnee-cscw2006.pdf">
          (Don’t Look Stupid: Avoiding Pitfalls when Recommending Research Papers)P23
         </a>
        </li>
        <li>
         <a href="http://files.grouplens.org/papers/mcnee-chi06-acc.pdf">
          (Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems) P20
         </a>
        </li>
        <li>
         <a href="http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/36955.pdf">
          (The Learning Behind Gmail Priority Inbox)p17
         </a>
        </li>
        <li>
         <a href="http://about.digg.com/blog/digg-recommendation-engine-updates">
          (Digg Recommendation Engine Updates) P16
         </a>
        </li>
        <li>
         <a href="http://www.facebook.com/instantpersonalization/">
          http://www.facebook.com/instantpersonalization/ P13
         </a>
        </li>
        <li>
         <a href="http://www.slideshare.net/plamere/music-recommendation-and-discovery">
          (PPT: Music Recommendation and Discovery) p12
         </a>
        </li>
        <li>
         <a href="http://stuyresearch.googlecode.com/hg-history/c5aa9d65d48c787fd72dcd0ba3016938312102bd/blake/resources/p293-davidson.pdf">
          (The Youtube video recommendation system) p9
         </a>
        </li>
        <li>
         <a href="http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf">
          http://cdn-0.nflximg.com/us/pdf/Consumer_Press_Kit.pdf p9
         </a>
        </li>
        <li>
         <a href="http://www.netflixprize.com/">
          http://www.netflixprize.com/ p8
         </a>
        </li>
        <li>
         <a href="http://thesearchstrategy.com/ebooks/an%20introduction%20to%20search%20engines%20and%20web%20navigation.pdf">
          An Introduction to Search Engines and Web Navigation p7
         </a>
        </li>
        <li>
         <a href="http://stanford2009.wikispaces.com/">
          课程：Data Mining and E-Business: The Social Data Revolution P7)
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          Cross Selling P6
         </a>
        </li>
        <li>
         <a href="http://readwrite.com/2009/01/26/recommender_systems">
          A Guide to Recommender System P4
         </a>
        </li>
        <li>
         <a href="http://www.kuqin.com/shuoit/20151130/349205.html">
          http://www.kuqin.com/shuoit/20151130/349205.html P1
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20Best%20Stu%20Paper%E3%80%91Incorporating%20Occupancy%20into%20Frequent%20Pattern%20Mini.pdf&amp;id=37992" target="_blank">
          【CIKM 2012 Best Stu Paper】Incorporating Occupancy into Frequent Pattern Mini.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91A%20Latent%20Pairwise%20Preference%20Learning%20Approach%20for%20Recomme.pdf&amp;id=37993" target="_blank">
          【CIKM 2012 poster】A Latent Pairwise Preference Learning Approach for Recomme.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91An%20Effective%20Category%20Classification%20Method%20Based%20on%20a%20Lan.pdf&amp;id=37994" target="_blank">
          【CIKM 2012 poster】An Effective Category Classification Method Based on a Lan.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Learning%20to%20Rank%20for%20Hybrid%20Recommendation.pdf&amp;id=37995" target="_blank">
          【CIKM 2012 poster】Learning to Rank for Hybrid Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Learning%20to%20Recommend%20with%20Social%20Relation%20Ensemble.pdf&amp;id=37996" target="_blank">
          【CIKM 2012 poster】Learning to Recommend with Social Relation Ensemble.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Maximizing%20Revenue%20from%20Strategic%20Recommendations%20under%20De.pdf&amp;id=37997" target="_blank">
          【CIKM 2012 poster】Maximizing Revenue from Strategic Recommendations under De.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91On%20Using%20Catexperts%20for%20Improving%20the%20Performance%20an.pdf&amp;id=37998" target="_blank">
          【CIKM 2012 poster】On Using Category Experts for Improving the Performance an.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Relation%20Regularized%20Subspace%20Recommending%20for%20Related%20Sci.pdf&amp;id=37999" target="_blank">
          【CIKM 2012 poster】Relation Regularized Subspace Recommending for Related Sci.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Top-N%20Recommendation%20through%20Belief%20Propagation.pdf&amp;id=38000" target="_blank">
          【CIKM 2012 poster】Top-N Recommendation through Belief Propagation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20poster%E3%80%91Twitter%20Hyperlink%20Recommendation%20with%20User-Tweet-Hyperlink.pdf&amp;id=38001" target="_blank">
          【CIKM 2012 poster】Twitter Hyperlink Recommendation with User-Tweet-Hyperlink.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91Automatic%20Query%20Expansion%20Based%20on%20Tag%20Recommendation.pdf&amp;id=38002" target="_blank">
          【CIKM 2012 short】Automatic Query Expansion Based on Tag Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91Graph-Based%20Workflow%20Recommendation-%20On%20Improving%20Business%20.pdf&amp;id=38003" target="_blank">
          【CIKM 2012 short】Graph-Based Workflow Recommendation- On Improving Business .pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91Location-Sensitive%20Resources%20Recommendation%20in%20Social%20Taggi.pdf&amp;id=38004" target="_blank">
          【CIKM 2012 short】Location-Sensitive Resources Recommendation in Social Taggi.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91More%20Than%20Relevance-%20High%20Utility%20Query%20Recommendation%20By%20M.pdf&amp;id=38005" target="_blank">
          【CIKM 2012 short】More Than Relevance- High Utility Query Recommendation By M.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91PathRank-%20A%20Novel%20Node%20Ranking%20Measure%20on%20a%20Heterogeneous%20G.pdf&amp;id=38006" target="_blank">
          【CIKM 2012 short】PathRank- A Novel Node Ranking Measure on a Heterogeneous G.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91PRemiSE-%20Personalized%20News%20Recommendation%20via%20Implicit%20Soci.pdf&amp;id=38007" target="_blank">
          【CIKM 2012 short】PRemiSE- Personalized News Recommendation via Implicit Soci.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91Query%20Recommendation%20for%20Children.pdf&amp;id=38008" target="_blank">
          【CIKM 2012 short】Query Recommendation for Children.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91The%20Early-Adopter%20Graph%20and%20its%20Application%20to%20Web-Page%20Rec.pdf&amp;id=38009" target="_blank">
          【CIKM 2012 short】The Early-Adopter Graph and its Application to Web-Page Rec.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91Time-aware%20Topic%20Recommendation%20Based%20on%20Micro-blogs.pdf&amp;id=38010" target="_blank">
          【CIKM 2012 short】Time-aware Topic Recommendation Based on Micro-blogs.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%20short%E3%80%91Using%20Program%20Synthesis%20for%20Social%20Recommendations.pdf&amp;id=38011" target="_blank">
          【CIKM 2012 short】Using Program Synthesis for Social Recommendations.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91A%20Decentralized%20Recommender%20System%20for%20Effective%20Web%20Credibility%20.pdf&amp;id=38012" target="_blank">
          【CIKM 2012】A Decentralized Recommender System for Effective Web Credibility .pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91A%20Generalized%20Framework%20for%20Reciprocal%20Recommender%20Systems.pdf&amp;id=38013" target="_blank">
          【CIKM 2012】A Generalized Framework for Reciprocal Recommender Systems.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91Dynamic%20Covering%20for%20Recommendation%20Systems.pdf&amp;id=38014" target="_blank">
          【CIKM 2012】Dynamic Covering for Recommendation Systems.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91Efficient%20Retri.%20of%20Recommendations%20in%20a%20Matrix%20Factorization%20.pdf&amp;id=38015" target="_blank">
          【CIKM 2012】Efficient Retrieval of Recommendations in a Matrix Factorization .pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91Exploring%20Personal%20Impact%20for%20Group%20Recommendation.pdf&amp;id=38016" target="_blank">
          【CIKM 2012】Exploring Personal Impact for Group Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91LogUCB-%20An%20Explore-Exploit%20Algorithm%20For%20Comments%20Recommendation.pdf&amp;id=38017" target="_blank">
          【CIKM 2012】LogUCB- An Explore-Exploit Algorithm For Comments Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91Metaphor-%20A%20System%20for%20Related%20Search%20Recommendations.pdf&amp;id=38018" target="_blank">
          【CIKM 2012】Metaphor- A System for Related Search Recommendations.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91Social%20Contextual%20Recommendation.pdf&amp;id=38019" target="_blank">
          【CIKM 2012】Social Contextual Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90CIKM%202012%E3%80%91Social%20Recommendation%20Across%20Multiple%20Relational%20Domains.pdf&amp;id=38020" target="_blank">
          【CIKM 2012】Social Recommendation Across Multiple Relational Domains.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90COMMUNICATIONS%20OF%20THE%20ACM%E3%80%91Recommender%20Systems.pdf&amp;id=38021" target="_blank">
          【COMMUNICATIONS OF THE ACM】Recommender Systems.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90ICDM%202012%20short___%E3%80%91Multiplicative%20Algorithms%20for%20Constrained%20Non-negative%20M.pdf&amp;id=38022" target="_blank">
          【ICDM 2012 short___】Multiplicative Algorithms for Constrained Non-negative M.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90ICDM%202012%20short%E3%80%91Collaborative%20Filtering%20with%20Aspect-based%20Opinion%20Mining-%20A.pdf&amp;id=38023" target="_blank">
          【ICDM 2012 short】Collaborative Filtering with Aspect-based Opinion Mining- A.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90ICDM%202012%20short%E3%80%91Learning%20Heterogeneous%20Similarity%20Measures%20for%20Hybrid-Recom.pdf&amp;id=38024" target="_blank">
          【ICDM 2012 short】Learning Heterogeneous Similarity Measures for Hybrid-Recom.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90ICDM%202012%20short%E3%80%91Mining%20Personal%20Context-Aware%20Preferences%20for%20Mobile%20Users.pdf&amp;id=38025" target="_blank">
          【ICDM 2012 short】Mining Personal Context-Aware Preferences for Mobile Users.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90ICDM%202012%E3%80%91Link%20Prediction%20and%20Recommendation%20across%20Heterogenous%20Social%20Networks.pdf&amp;id=38026" target="_blank">
          【ICDM 2012】Link Prediction and Recommendation across Heterogenous Social Networks.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90IEEE%20Computer%20Society%202009%E3%80%91Matrix%20factorization%20techniques%20for%20recommender%20.pdf&amp;id=38027" target="_blank">
          【IEEE Computer Society 2009】Matrix factorization techniques for recommender .pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90IEEE%20Consumer%20Communications%20and%20Networking%20Conference%202006%E3%80%91FilmTrust%20movie.pdf&amp;id=38028" target="_blank">
          【IEEE Consumer Communications and Networking Conference 2006】FilmTrust movie.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90IEEE%20Trans%20on%20Audio%2C%20Speech%20and%20Laguage%20Processing%202010%E3%80%91Personalized%20music%20.pdf&amp;id=38029" target="_blank">
          【IEEE Trans on Audio, Speech and Laguage Processing 2010】Personalized music .pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90IEEE%20Transactions%20on%20Knowledge%20and%20Data%20Engineering%202005%E3%80%91Toward%20the%20next%20ge.pdf&amp;id=38030" target="_blank">
          【IEEE Transactions on Knowledge and Data Engineering 2005】Toward the next ge.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90INFOCOM%202011%E3%80%91Bayesian-inference%20Based%20Recommendation%20in%20Online%20Social%20Network.pdf&amp;id=38031" target="_blank">
          【INFOCOM 2011】Bayesian-inference Based Recommendation in Online Social Network.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90KDD%202009%E3%80%91Learning%20optimal%20ranking%20with%20tensor%20factorization%20for%20tag%20recomme.pdf&amp;id=38032" target="_blank">
          【KDD 2009】Learning optimal ranking with tensor factorization for tag recomme.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202009%E3%80%91Learning%20to%20Recommend%20with%20Social%20Trust%20Ensemble.pdf&amp;id=38033" target="_blank">
          【SIGIR 2009】Learning to Recommend with Social Trust Ensemble.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Adaptive%20Diversification%20of%20Recommendation%20Results%20via%20Latent%20Fa.pdf&amp;id=38034" target="_blank">
          【SIGIR 2012】Adaptive Diversification of Recommendation Results via Latent Fa.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Collaborative%20Personalized%20Tweet%20Recommendation.pdf&amp;id=38035" target="_blank">
          【SIGIR 2012】Collaborative Personalized Tweet Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Dual%20Role%20Model%20for%20Question%20Recommendation%20in%20Community%20Questio.pdf&amp;id=38036" target="_blank">
          【SIGIR 2012】Dual Role Model for Question Recommendation in Community Questio.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Exploring%20Social%20Influence%20for%20Recommendation%20-%20A%20Generative%20Mod.pdf&amp;id=38037" target="_blank">
          【SIGIR 2012】Exploring Social Influence for Recommendation – A Generative Mod.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Increasing%20Temporal%20Diversity%20with%20Purchase%20Intervals.pdf&amp;id=38038" target="_blank">
          【SIGIR 2012】Increasing Temporal Diversity with Purchase Intervals.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Learning%20to%20Rank%20Social%20Update%20Streams.pdf&amp;id=38039" target="_blank">
          【SIGIR 2012】Learning to Rank Social Update Streams.pdf
         </a>
        </li>
        <li>
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          【SIGIR 2012】Personalized Click Shaping through Lagrangian Duality for Online.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91Predicting%20the%20Ratings%20of%20Multimedia%20Items%20for%20Making%20Personaliz.pdf&amp;id=38041" target="_blank">
          【SIGIR 2012】Predicting the Ratings of Multimedia Items for Making Personaliz.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91TFMAP-Optimizing%20MAP%20for%20Top-N%20Context-aware%20Recommendation.pdf&amp;id=38042" target="_blank">
          【SIGIR 2012】TFMAP-Optimizing MAP for Top-N Context-aware Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGIR%202012%E3%80%91What%20Reviews%20are%20Satisfactory-%20Novel%20Features%20for%20Automatic%20Help.pdf&amp;id=38043" target="_blank">
          【SIGIR 2012】What Reviews are Satisfactory- Novel Features for Automatic Help.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91%20A%20Semi-Supervised%20Hybrid%20Shilling%20Attack%20Detector%20for%20Trustwor.pdf&amp;id=38044" target="_blank">
          【SIGKDD 2012】 A Semi-Supervised Hybrid Shilling Attack Detector for Trustwor.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91%20RecMax-%20Exploiting%20Recommender%20Systems%20for%20Fun%20and%20Profit.pdf&amp;id=38045" target="_blank">
          【SIGKDD 2012】 RecMax- Exploiting Recommender Systems for Fun and Profit.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91Circle-based%20Recommendation%20in%20Online%20Social%20Networks.pdf&amp;id=38046" target="_blank">
          【SIGKDD 2012】Circle-based Recommendation in Online Social Networks.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91Cross-domain%20Collaboration%20Recommendation.pdf&amp;id=38047" target="_blank">
          【SIGKDD 2012】Cross-domain Collaboration Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91Finding%20Trending%20Local%20Topics%20in%20Search%20Queries%20for%20Personaliza.pdf&amp;id=38048" target="_blank">
          【SIGKDD 2012】Finding Trending Local Topics in Search Queries for Personaliza.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91GetJar%20Mobile%20Application%20Recommendations%20with%20Very%20Sparse%20Datasets.pdf&amp;id=38049" target="_blank">
          【SIGKDD 2012】GetJar Mobile Application Recommendations with Very Sparse Datasets.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91Incorporating%20Heterogenous%20Information%20for%20Personalized%20Tag%20Rec.pdf&amp;id=38050" target="_blank">
          【SIGKDD 2012】Incorporating Heterogenous Information for Personalized Tag Rec.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90SIGKDD%202012%E3%80%91Learning%20Personal%2BSocial%20Latent%20Factor%20Model%20for%20Social%20Recomme.pdf&amp;id=38051" target="_blank">
          【SIGKDD 2012】Learning Personal+Social Latent Factor Model for Social Recomme.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90VLDB%202012%E3%80%91Challenging%20the%20Long%20Tail%20Recommendation.pdf&amp;id=38052" target="_blank">
          【VLDB 2012】Challenging the Long Tail Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90VLDB%202012%E3%80%91Supercharging%20Recommender%20Systems%20using%20Taxonomies%20for%20Learning%20U.pdf&amp;id=38053" target="_blank">
          【VLDB 2012】Supercharging Recommender Systems using Taxonomies for Learning U.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202012%20Best%20paper%E3%80%91Build%20Your%20Own%20Music%20Recommender%20by%20Modeling%20Internet%20R.pdf&amp;id=38054" target="_blank">
          【WWW 2012 Best paper】Build Your Own Music Recommender by Modeling Internet R.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91A%20Personalized%20Recommender%20System%20Based%20on%20User%5C%26%23039%3Bs%20Informatio.pdf&amp;id=38055" target="_blank">
          【WWW 2013】A Personalized Recommender System Based on User’s Informatio.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Diversified%20Recommendation%20on%20Graphs-Pitfalls%2C%20Measures%2C%20and%20Algorithms.pdf&amp;id=38056" target="_blank">
          【WWW 2013】Diversified Recommendation on Graphs-Pitfalls, Measures, and Algorithms.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Do%20Social%20Explanations%20Work-Studying%20and%20Modeling%20the%20Effects%20of%20S.pdf&amp;id=38057" target="_blank">
          【WWW 2013】Do Social Explanations Work-Studying and Modeling the Effects of S.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Generation%20of%20Coalition%20Structures%20to%20Provide%20Proper%20Groups%5C%26%23039%3B.pdf&amp;id=38058" target="_blank">
          【WWW 2013】Generation of Coalition Structures to Provide Proper Groups’.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Learning%20to%20Recommend%20with%20Multi-Faceted%20Trust%20in%20Social%20Networks.pdf&amp;id=38059" target="_blank">
          【WWW 2013】Learning to Recommend with Multi-Faceted Trust in Social Networks.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Multi-Label%20Learning%20with%20Millions%20of%20Labels-Recommending%20Advertis.pdf&amp;id=38060" target="_blank">
          【WWW 2013】Multi-Label Learning with Millions of Labels-Recommending Advertis.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Personalized%20Recommendation%20via%20Cross-Domain%20Triadic%20Factorization.pdf&amp;id=38061" target="_blank">
          【WWW 2013】Personalized Recommendation via Cross-Domain Triadic Factorization.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Profile%20Deversity%20in%20Search%20and%20Recommendation.pdf&amp;id=38062" target="_blank">
          【WWW 2013】Profile Deversity in Search and Recommendation.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Real-Time%20Recommendation%20of%20Deverse%20Related%20Articles.pdf&amp;id=38063" target="_blank">
          【WWW 2013】Real-Time Recommendation of Deverse Related Articles.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Recommendation%20for%20Online%20Social%20Feeds%20by%20Exploiting%20User%20Response.pdf&amp;id=38064" target="_blank">
          【WWW 2013】Recommendation for Online Social Feeds by Exploiting User Response.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Recommending%20Collaborators%20Using%20Keywords.pdf&amp;id=38065" target="_blank">
          【WWW 2013】Recommending Collaborators Using Keywords.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Signal-Based%20User%20Recommendation%20on%20Twitter.pdf&amp;id=38066" target="_blank">
          【WWW 2013】Signal-Based User Recommendation on Twitter.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91SoCo-%20A%20Social%20Network%20Aided%20Context-Aware%20Recommender%20System.pdf&amp;id=38067" target="_blank">
          【WWW 2013】SoCo- A Social Network Aided Context-Aware Recommender System.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Tailored%20News%20in%20the%20Palm%20of%20Your%20HAND-A%20Multi-Perspective%20Transpa.pdf&amp;id=38068" target="_blank">
          【WWW 2013】Tailored News in the Palm of Your HAND-A Multi-Perspective Transpa.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91TopRec-Domain-Specific%20Recommendation%20through%20Community%20Topic%20Mini.pdf&amp;id=38069" target="_blank">
          【WWW 2013】TopRec-Domain-Specific Recommendation through Community Topic Mini.pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91User%5C%26%23039%3Bs%20Satisfaction%20in%20Recommendation%20Systems%20for%20Groups-an%20.pdf&amp;id=38070" target="_blank">
          【WWW 2013】User’s Satisfaction in Recommendation Systems for Groups-an .pdf
         </a>
        </li>
        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Using%20Link%20Semantics%20to%20Recommend%20Collaborations%20in%20Academic%20Socia.pdf&amp;id=38071" target="_blank">
          【WWW 2013】Using Link Semantics to Recommend Collaborations in Academic Socia.pdf
         </a>
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        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=%E3%80%90WWW%202013%E3%80%91Whom%20to%20Mention-Expand%20the%20Diffusion%20of%20Tweets%20by%20%40%20Recommendation.pdf&amp;id=38072" target="_blank">
          【WWW 2013】Whom to Mention-Expand the Diffusion of Tweets by @ Recommendation.pdf
         </a>
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        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=Recommender%2BSystems%2BHandbook.pdf&amp;id=38073" target="_blank">
          Recommender+Systems+Handbook.pdf
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        <li>
         <a href="http://blog.sciencenet.cn/home.php?mod=attachment&amp;filename=tutorial.pdf&amp;id=38074" target="_blank">
          tutorial.pdf
         </a>
        </li>
       </ul>
       <h2>
        各个领域的推荐系统
       </h2>
       <p>
        <strong>
         图书
        </strong>
       </p>
       <ul>
        <li>
         Amazon
        </li>
        <li>
         豆瓣读书
        </li>
        <li>
         当当网
        </li>
       </ul>
       <p>
        <strong>
         新闻
        </strong>
       </p>
       <ul>
        <li>
         Google News
        </li>
        <li>
         Genieo
        </li>
        <li>
         Getprismatic
         <a href="http://getprismatic.com/">
          http://getprismatic.com/
         </a>
        </li>
       </ul>
       <p>
        <strong>
         电影
        </strong>
       </p>
       <ul>
        <li>
         Netflix
        </li>
        <li>
         Jinni
        </li>
        <li>
         MovieLens
        </li>
        <li>
         Rotten Tomatoes
        </li>
        <li>
         Flixster
        </li>
        <li>
         MTime
        </li>
       </ul>
       <p>
        <strong>
         音乐
        </strong>
       </p>
       <ul>
        <li>
         豆瓣电台
        </li>
        <li>
         Lastfm
        </li>
        <li>
         Pandora
        </li>
        <li>
         Mufin
        </li>
        <li>
         Lala
        </li>
        <li>
         EMusic
        </li>
        <li>
         Ping
        </li>
        <li>
         虾米电台
        </li>
        <li>
         Jing.FM
        </li>
       </ul>
       <p>
        <strong>
         视频
        </strong>
       </p>
       <ul>
        <li>
         Youtube
        </li>
        <li>
         Hulu
        </li>
        <li>
         Clciker
        </li>
       </ul>
       <p>
        <strong>
         文章
        </strong>
       </p>
       <ul>
        <li>
         CiteULike
        </li>
        <li>
         Google Reader
        </li>
        <li>
         StumbleUpon
        </li>
       </ul>
       <p>
        <strong>
         旅游
        </strong>
       </p>
       <ul>
        <li>
         Wanderfly
        </li>
        <li>
         TripAdvisor
        </li>
       </ul>
       <p>
        <strong>
         社会网络
        </strong>
       </p>
       <ul>
        <li>
         Facebook
        </li>
        <li>
         Twitter
        </li>
       </ul>
       <p>
        <strong>
         综合
        </strong>
       </p>
       <ul>
        <li>
         Amazon
        </li>
        <li>
         GetGlue
        </li>
        <li>
         Strands
        </li>
        <li>
         Hunch
        </li>
       </ul>
       <p>
        来源：酷勤网
       </p>
      </div>
      <div>
       <strong>
        注：转载文章均来自于公开网络，仅供学习使用，不会用于任何商业用途，如果侵犯到原作者的权益，请您与我们联系删除或者授权事宜，联系邮箱：contact@dataunion.org。转载数盟网站文章请注明原文章作者，否则产生的任何版权纠纷与数盟无关。
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